METHOD FOR DETERMINING A PERIODIC WAVE ITERACY OF PHOTOPLETISMOGRAM FOR BIOMETRIC IDENTIFICATION
Background. In recent years, the development of an automated identification process using biometric authentication has been observed, which has a high level of protection, since it allows you to evaluate the physical parameters and characteristics of a particular person. Such access control is more reliable since identifiers cannot be transferred to third parties or duplicated to bypass security systems. Over the past decades, a significant number of systems with biometric identification has been developed, but systems with identification according to the characteristics of the photoplethysmogram still receive little attention. The main task of biometric personality identification using photoplethysmogram is the search and implementation of machine learning methods to determine their belonging to a particular patient.
Objective. The purpose of the paper is to develop an algorithm for distinguishing pulse wave iterations using the calculation of the temporal characteristics of photoplethysmograms, such as the maximum amplitude value, variance, mean absolute deviation, Wilson amplitude and the total sum of signal amplitude values.
Methods. Based on the study of the temporal characteristics of the photoplethysmogram, an algorithm for distinguishing pulse wave iterations is created, which can be used for further biometric identification of a person using machine-learning methods.
Results. The results can be used for further development of automated access control and management systems using biometric identification.
Conclusions. Known methods of biometric identification are usually based on the static parameters of a person (the structure of the cornea of the eye, palm, fingerprints, geometry of the auricle, etc.), but have a low level of protection, since using special equipment you can create a copy of the biometric key. Therefore, today, the use of methods based on the parameters of dynamic biometric identification (plethysmogram, cardiogram and others) provides the highest degree of protection, but requires a more accurate software device to isolate and determine common symptoms. The proposed approach to calculating individual parameters of the photoplethysmogram with the aim of their subsequent classification by machine learning methods may be an acceptable solution for patient biometric identification systems.
Full Text:PDF (Українська)
C. El-Hajj and P. Kyriacou, “A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure”, Biomedical Signal Processing and Control, vol. 58, p. 101870, 2020. doi: 10.1016/j.bspc.2020.101870
S. Khalid et al., “Blood pressure estimation using photoplethysmography only: Comparison between different machine learning approaches”, J. Healthcare Eng., vol. 2018, pp. 1–13, 2018. doi: 10.1155/2018/1548647
А. Holovyna et al., “Analysis of the similarity of pulse waves in photoplethysmograms”, VMK MHU ymeny M.V. Lomonosova, vol. 53, no. 1, pp. 46–58, 2016.
Y. Mamontov, “Analysis and classification of photoplethysmograms using the Hopfield neural network”, Vestnik Novosibirskogo Gosudarstvennogo Universiteta. Seriya: Informaczionnye Tekhnologii, vol. 12, no. 4, pp. 53–58, 2014.
S. Bashar et al., “A machine learning approach for heart rate estimation from PPG signal using random forest regression algorithm”, in Proc. 2019 Int. Conf. Electrical, Computer and Communication Engineering, Cox’sBazar, Bangladesh, 2019, pp. 1–5. doi: 10.1109/ECACE.2019.8679356
Y. Zhang and Z. Feng, “A SVM method for continuous blood pressure estimation from a PPG signal”, in Proc. 9th Int. Conf. Machine Learning and Computing, 2017. doi: 10.1145/3055635.3056634
S. Yang et al., “Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning”, in Proc. IET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence, 2018. doi: 10.1049/cp.2018.1721
V. Jindal et al., “An adaptive deep learning approach for PPG-based identification”, in Proc. 38th Int. Conf. IEEE Engineering in Medicine and Biology Society, 2016. doi: 10.1109/embc.2016.7592193
H. Lim et al., “A deep neural network-based pain classifier using a photoplethysmography signal”, Sensors, vol. 19, no. 2, p. 384, 2019. doi: 10.3390/s19020384
I. Yakovenko et al., “Improvement of the credibility of analysis of electrocardiograms for biometric personal identification”, Perspective Technologies and Devices, no. 15, pp. 125–130, 2020. doi: 10.36910/6775-2313-5352-2019-15-18
I. Yakovenko et al., “Biometrical identification on the basis of photoplethysmogram for automated medical systems”, Perspective Technologies and Devices, no. 15, pp. 120–124, 2020. doi: 10.36910/6775-2313-5352-2019-15-17
K. Vonsevych et al., “Evaluation of electromyogram time characteristics of the wrist functional movements for intuitive control of bionic prosthesis”, Naukovi Visti NTUU KPI, no. 1, pp. 45–53, 2018. doi: 10.20535/1810-0546.2018.1.115941
K. Vonsevych et al., “Fingers movements control system based on artificial neural network model”, Radioelectronics and Communications Systems, vol. 62, no. 1, pp. 23–33, 2019. doi: 10.3103/s0735272719010047
GOST Style Citations
- There are currently no refbacks.
Copyright (c) 2020 The Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.